PM2.5 Concentration Prediction Based on VE-GEP Algorithm
Accurate prediction of PM2.5 concentration is essential for public health and environmental protection,but its nonlinearity,variability,and complexity make it difficult.Based on this,this study proposes a gene expression programming algorithm based on virus evolution(VE-GEP)to predict PM2.5 concentration in response to the shortcomings of traditional GEP.The algorithm introduces a resurrection mechanism and a mutagenic restart mechanism based on GEP.The resurrection mechanism removes poor-quality individuals from the population and improves individual quality in the population.The mutagenic restart mechanism increases population diversity and enhances algorithm optimization-seeking ability by introducing high-quality genes and new individuals.Experimental results show that the VE-GEP algorithm improves the prediction models to different degrees compared to GEP,DSCE-GEP,and CNN-LSTM in spring,summer,and fall,with improvements in the fitness of 1.28%/0.1%/0.13%,1.86%/1.29%/0.42%,and 0.57%/0.24%/0.29%,respectively,which provides new ideas and methods for PM2.5 concentration prediction studies.
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